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SSN UCs / RequirementsSensor metadata: It should be possible to include metadata about the sensors producing the observations.CRS definition: The URI of the coordinate reference system (CRS) shall be specified when geographic coordinates are present in the data. Georeferenced sensor data: It should possible to georeference observations.Positioning system: It should be possible to define the positioning system used to determine the spatial location in the data.Model reuse: Spatial data modelling issues solved in existing observation models shall be considered for adoption, e.g. O&M.Reference external vocabularies: It should be possible to refer to externally-managed controlled vocabularies.Observation aggregations: It should be possible to represent aggregations of observations.Mobile sensors: It should be possible to represent sensors that change their location, as well as the current location of the sensor at the observation time.Current location: It should be possible to get/represent the current location of the sensor providing the observation".Moving features: It should be possible to refer to features that change their location.Linkability: It should be possible to link to other data on the Web.Nominal observations: It should be possible to represent qualitative and nominal observations.Space-time multi-scale: It should be possible to represent and integrate data over spatial and temporal scales.Provenance: It should be possible to add provenance metadata.Multilingual support: It should be possible to add metadata in different languages.dependency between data and actuation decision (out of scope?)Humans as sensors: It should be possible to represent observations taken by human individuals or communities acting as sensors perceiving the environment.Lightweight API: A lightweight API is needed for implementation on IoT devices.Time series: It must be possible to represent time series of sensor data (see also coverage)Spatial vagueness: It should be possible to describe locations in a vague, imprecise manner. For instance, to represent spatial descriptions from crowdsourced observations, such as "we saw a wildfire at the bottom of the hillside" or "we felt a light tremor while walking by Los Angeles downtown". Another related use cases deal with spatial locations identified in historical texts, e.g. a battle occurred at the south west boundary of the Roman Empire.3D support: It must be possible to represent locations of sensors and observations in 3D space (included in Best Practice reqs, column P).Virtual observations: It must be possible to represent synthetic observations made by computational procedures or inference.Ex-situ sampling: It should be possible to represent ex-situ (remote) sampling or sensing (as opposite to in-situ; defined in UC32)Sampling topology: It should be possible to represent topological relationships between observation samples, e.g. specimens located along a borehole or probe spots found on a polished section of rocks.Uncertainty in observations: It must be possible to represent uncertainty in observations.Sensing procedure: It should be possible to attach the procedural description of a sensing method.Profiling, e.g. for checking compliance to standard model.Models as sensorsDynamic sensor data: It should be possible to represent near real-time streaming sensor measurements.
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2. Meteorological Data Rescue Use Casequality metadatayesyesyesyesyesyesyes
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3. Habitat zone verification for designation of Marine Conservation Zonesyesyese.g. videoyesyesyesyesala O&M. Fine-grainedyesyesyesyes
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4. Real-time Wildfire Monitoringyesyesyeslink to social media, processes and other sourcesyes
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5. Diachronic Burnt Scar Mappingyesyesyeslink to social media
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12. Integration of governmental and utility data to enable smart gridsyesyes
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14. Publication of air quality data aggregationsyesyesyes
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15. Publication of transport card validation and recharging datayes
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18. Various Sensor Use Cases
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21. Publication of Raw Subsurface Monitoring Datayesyesyesyesyesyesyes, e.g. by time unit or depth unit.yesyes, e.g. qualitative values for lithology.yes, e.g. public perception of ground tremors.yesyesyesyes, e.g. a seismic event is calculated as originating from a subsurface location following observations at various surface sensors.yesyes, and uncertainty can include upper and lower limits of detectability/recording, though this could be detailed in the procedural description.yesyes
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22. Use of a place name ontology for geo-parsing text and geo-enabling searchesyes
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23. Driving to work in the snowdiscoverylink observation to features in space and time. ssn:featureOfInterest?yesyesyesyesuser behaviouryesyes
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24. Intelligent Transportation Systemyesyesyesyesyes
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25. Optimizing energy consumption, production, sales and purchases in Smart Gridsyes
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26 "101" Smart City Use-casesyesyes, how to update frequently?yesyesyesyes
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28. Images, e.g. a Time series of a Water Courseyesyese.g. photosyesyesyesyesyestimeseries of imagesyes
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29. Droughts in geological complex environments where groundwater is important yesyesyesyesyesyesyes
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30. Soil data applicationsyes, e.g. O&M.yesyesyesyesyesyes
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31. Bushfire response coordination centreyes - related to placeyesyesyesyes
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32. Observations on geological samplesyesyesyes; specimen model provided in O&M. Also consider PROV-O.yesyesyes, in this case we are looking at provenance of real world things.yesyesyesyes
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33. Spatial Samplingyesyesyesyesyes, e.g. O&M.yesyes, e.g. representations of real world features.yesyesyesyes
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35. Satellite data processingyesyesyesyes; satellite position.yesyesyesyesyesyesyes; validation of data completeness.
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36. Marine observations - eMIIyesyesyesyesyesyes
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37. Marine observations - data providersyesyesyes; qualitative observations.yesyes
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38. Marine observations - data consumersyes
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41. Metadata and Search Granularityyesyesyes
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42. Crowdsourced earthquake observation informationyesyesyesyesyesyesyesyesyesyesyesyesyes
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44. Crop Yield Estimation using multiple satellitesyesyesyesyesyesyesyesyesyes
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49. Creation of “virtual observations” from “analysis” phase of weather prediction modelyesyesyesyesyesyesyesyesyesyesyes
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